In a large brick-and-mortar retail store setting, it can be a daunting task for a customer to rapidly locate the exact product he/she is looking for. The same is true for the customer to navigate through the store, find a free store associate to assist in shopping, or for a store associate to identify the customer who needs help. With the increase of store foot traffic, these issues are even more prominent.
On the other hand, although online customer profiling has been made easy with analytic tools, offline customer profile building remains a blank. Unless a customer actually purchases something, information about non-transactional behaviors such as looking around, trying a product, interacting with store associates, etc., is typically not captured at all. This non-transactional information, however, is very valuable in acquiring new customers, conducting targeted marketing, product recommendation, and cross/upselling to existing customers. Moreover, offline customer behavior profiling can offer accurate and invaluable insight of a customer or potential customer's lifestyle, preference, shopping pattern and so on. For an integrated retailer, this info can be shared across all channels as to fully explore sales opportunities.
Many companies have tried to bridge the divide between a customer's online and offline shopping behavior by bringing the offline shopping experience online through the use of mobile smartphone applications and printed interfaces for mobile commerce that utilize a Quick Response (QR) Code that provides a URL to eCommerce websites in order for the user to conduct an eCommerce transaction. In this type of a system, a customer uses a smartphone camera and a QR code scanner application to scan the QR code. However, such code recognition can be a painful process, which undermines the user experience. For example, multiple QR code standards prevail, which only adds to the complexity of the implementations of the scanner apps and QR codes. Further, the requirements of a dedicated scanning app, difficulty in aligning the camera due to changing lighting conditions and focusing can detract from the customer experience.
QR codes are also limited to about 7 KB of data, which limits the information contained in a QR code to a URL or simple textual info. QR codes also does not allow for localized interactions between the user and the printed interface; they waste precious space on printed surfaces; and they are subject to modifications and damages rendering the code instantly disabled.
Another way in which retailers have attempted to capture offline behavior is through indoor, location-based tracking. While there are a handful of location-based products available in the market today for various mobile platforms, there does not yet exist an end-to-end platform-based solution that is dedicated towards indoor positioning and indoor navigation. In particular, indoor positioning and navigation have long been a challenging area due primarily to the unavailability of GPS signals indoor, the prohibitive cost of implementation, and the inaccuracy of indoor navigation schemes based on Cellular-ID or Wireless LAN (theoretically, conventional WiFi-based fingerprinting approach can achieve an accuracy of about 1.5 meter, and therefore does not provide product-level granularity).
For example, one such indoor location-based product utilizes radio map fingerprinting, which is a process that captures the impression of the signals of various radio transmitters and generates a signature of such impression. Such an indoor positioning system relies on a pre-populated geo-spatial database that contains numerous waypoints that represent intermediate routing nodes. Georeferencing, i.e. adding coordinates to these nodes, is a time consuming and error-prone task that involves significant manual work and alignment. Oftentimes, the end results are waypoints that are not aligned on a straight line or not at a predefined interval as they are intended to be. This translates into increased labor costs associated with establishing and maintaining such indoor location-based services, hence impacting the bottom line of a business.
In an integrated retail setting, there is currently no mature way to accurately and automatically check-in a customer for either in-store shopping or merchandise pickup for online orders. Existing approaches that rely on geofences or smartphone APIs (such as those provided by Foursquare™) cannot guarantee that a customer actually checks in. Geofencing has only fussy knowledge about the customer's location relative to the store, as anything within the coverage of a geofence (known as proximity) is considered in range, and a customer can check in anywhere within the circle, even if the customer is not actually physically in the store (such as in the parking lot or elsewhere in the mall, including at a competitor's store). On the other hand, using conventional smartphone APIs for check-in can be misleading as it can lead to faked data. Not only is the precision of locations determined by such applications very coarse, but some applications also allow “virtual check-ins” without physical presence of the customer.